These are registration processes which create extensive imaging possibilities to assist a treating doctor during a medical intervention or surgical procedure. Image data sets are regularly acquired preoperatively using image acquisition systems which show the image area of interest in an optimum manner. During an operation, additional images are then acquired which in themselves do not necessarily provide sufficient information. The previously acquired image data is now registered to the image data acquired during the operation. The term registration means that a rule for mapping between the coordinate system of the first image data set and the coordinate system of the second image data set is determined. The two different image acquisition systems used for generating the two image data sets can only be related to one another if the patient is in a fixed basic position in each case because of the image information. For positionally and dimensionally correct mapping of the coordinate systems to one another, image recognition is often used in order to be able to recognize structures in the image data. The registration of two 3D image data sets to one another is generally known. An overview of this may be found in, for example, the book by J. V. Hajnal, D. L. G. Hill, D. J. Hawkes “Medical Image Registration”, CRC Press, 2001. The image acquisition systems used to generate the individual 3D image data sets may be different. Thus, for example, the registration of a 3D image data set acquired using an MRI scanner to a 3D rotation angiography image data set is known. Also, 2D/3D registration, i.e. the registration of a 3D image data set to 2D images is known, see e.g. the dissertation by P. Penney, “Registration of Tomographic Images to X-ray Projections for Use in Image Guided Interventions”, University of London, 1999, pages 36 to 58 and pages 97 to 159.
To use image recognition for registration it is necessary for the image structures in the image data to be recognizable. Often, however, the areas of interest in the patient are soft parts. A classical example of this is a patient's heart. Although soft parts can be well imaged by MRI, they are difficult to recognize on X-ray images. Registration between preoperatively and intraoperatively acquired cardiac image data is therefore ruled out if the intraoperatively acquired image data is obtained using an X-ray image acquisition system. It is helpful if, along with the image data relating to the soft parts, bones such as the spinal column, for example, are imaged. In the case of MRI, however, the image volume which can be acquired within typical acquisition times of 20 seconds is too small: heart and spinal column cannot be visualized simultaneously in one image. Extending the acquisition time is possible only with difficulty, as the patient has to hold his breath during the scan. Moreover, the imaging of muscle tissue such as cardiac muscle tissue requires the use of specific image acquisition parameters for MRI and the imaging of bones the use of quite different image acquisition parameters. If an image is to contain both bone and muscle tissue, a compromise would have to made, leading to unsatisfactory results.
After a mapping rule has been determined by the registration process, the preoperatively acquired image data and the intraoperatively acquired image data are merged into one visualization. In Siemens Medical, this can be effected by the syngo iPilot system. Details concerning syngo iPilot are described e.g. in the two-page leaflet “syngo iPilot—Effective guidance during interventional procedures” dated November 2005 which is available from Siemens Medical Solutions.
It would be desirable if the high-quality image data for soft parts which can be acquired by MRI (and also e.g. using 3D ultrasound imaging systems) could be used in some way to assist the treating doctor, which necessitates correlation with intraoperatively acquired image data.